# Spatial Interpolation

## Definition

Spatial interpolation is a method used in Geographic Information Systems (GIS) that estimates the values of data points at an un-sampled site within an area, based on sampled points from around that area. Spatial interpolation assumes that the things that are close to one another are more alike than those that are farther apart. This concept is known as "Tobler's First Law of Geography" and is the primary principle underlying the concept of spatial interpolation.## What is Spatial Interpolation?

Spatial interpolation is used to predict values for cells in a raster from a limited number of sample data points. It can be linear or non-linear and depends on the method employed. The goal of spatial interpolation is to provide a complete surface derived from a limited set of sample points.There are various methods used in spatial interpolation, including:

**Nearest Neighbor:**This method uses the value of the nearest point and assumes it for the unknown areas.**Inverse Distance Weighting (IDW):**It assumes that the points that are closer have more resemblance to each other than to those further apart.**Kriging:**This method forms an estimator that is both linear and unbiased, allowing control over the smoothness of the resulting surface.**Spline:**It involves the fitting of a mathematical function that minimizes overall surface curvature, resulting in the smooth surface that runs exactly through the input points.

Spatial interpolation plays a crucial role in geostatistics, meteorology, environmental science, and various other fields where geographical data are collected and analyzed.